CN105447658A - Voltage collapse point calculation method comprising wind power random fuzzy injection power system fluctuation - Google Patents

Abstract

The invention discloses a method for calculating the voltage collapse point of power system fluctuations including random fuzzy injection of wind power, which relates to the field of power systems. In the power system of random fuzzy injection of wind power output, the PV characteristic curve of the galloping system is obtained according to the continuous power flow, and each step is found out. The voltage collapse point of the PV curve and respectively fit the probability distribution of the voltage value V _cr and the lambda value at the collapse point; and then find out the membership function of the probability distribution function parameters by multiple fitting, and use the chance measure to compare The random fuzzy characteristics of the voltage collapse point are described, and the uncertainty description of the location of the fluctuating voltage collapse point is obtained. This method can provide a new idea for obtaining the static security domain of the power system in the future, provide a basis for obtaining a power system security domain that is more in line with the actual wind power injection, and provide an important reference for power dispatchers to carry out safe and stable operation of the power system.

Description

A Method for Calculating the Voltage Collapse Point of Power System Fluctuations Containing Random Fuzzy Injection of Wind Power

technical field

The invention belongs to the field of safe and stable operation of power systems, and considers multiple uncertainties of wind power injected into the power system, a method for solving fluctuating voltage collapse points.

Background technique

With people's demand for new energy development, wind power has been valued as a clean and renewable energy. The total installed capacity of wind power in the current power system continues to rise, and the use of wind power is also continuing to follow up. The impact of multiple uncertainties of wind power output on the power grid is also becoming more and more obvious. Considering the impact of wind power output on the static security of the power grid is very important for maintaining the security and stability of the power system and improving the utilization of wind power.

The literature "Review on the Impact of Wind Power Uncertainty on Power Systems" pointed out that uncertainty has both randomness and fuzziness, and the uncertainty of wind power output also takes into account these two aspects, while traditional methods generally only consider its randomness or fuzziness. , which may amplify the impact of uncertainty subjectively. The existing research methods on the static security region of the power system are generally studied from the deterministic output scenario, and the power system security region is obtained by obtaining the voltage collapse point through the continuous power flow method. The document "Anew method for the construction of maximal steady-state security regions of power systems" considers the uncertainty of the operating point caused by the randomness of wind power to obtain its security or safety probability; The randomness of wind power, the upper and lower limits of wind power output are used to obtain the static security domain of the system. These are to obtain the static security region of the power system by obtaining the deterministic voltage collapse point, and no research has been carried out from the perspective of the voltage collapse point of the fluctuating voltage collapse point that considers the random fuzzy injection of wind power active power into the power system.

In order to better combine the characteristics of the power system with the concept of developing clean and renewable energy, the characteristics of the static security domain of the power system with random fuzzy injection of wind power active power are studied, and the voltage collapse point of the power system with random fuzzy injection of wind power is studied. Characteristic research is very necessary. It can be used as an important reference for dispatchers to make full use of wind power resources and ensure the safety of system operation, and it is of great significance to gradually improve the utilization rate of wind power in the system.

Contents of the invention

In view of the large-scale wind turbine connected to the power system, the uncertainty of its output will cause hidden dangers to the safe and stable operation of the system. It is necessary to conduct research on the impact of wind turbines connected to the system security and stability domain of the power system. The traditional point of view is that the static security region of the power system based on the fixed system output mode is definite, which obviously cannot be applied to the real-time uncertain situation of wind power output. In the case of random fuzzy and uncertain output of wind power, the present invention obtains the PV characteristic curve of the galloping system, extracts the probability distributions of the voltage value V _cr and the lambda value at the voltage collapse point, and finds out their membership functions to estimate its reasonableness.

To achieve the above object, the present invention takes the following technical solutions:

The present invention solves the technical scheme that the above-mentioned problem takes:

1. Stochastic fuzzy uncertainty modeling of wind power output. Establish a random fuzzy uncertainty model of wind speed, select a group of shape parameters k and scale parameters c that meet the requirements to obtain random fuzzy wind speed; then according to the wind power output model, get wind power random fuzzy output;

2. Considering the random fuzzy continuous power flow calculation of wind power connected to the power system. Carry out a continuous power flow calculation corresponding to a wind power output to obtain a PV curve, and find the voltage value V _cr and λ value at the voltage collapse point; according to a group of wind power random fuzzy output values obtained in step 1, all wind power output can be obtained The PV curve under the level, the voltage value V _cr and the lambda value at a set of voltage collapse points;

3. Respectively fitting the probability distributions of the voltage value V _cr and the lambda value at the voltage collapse point. According to the obtained voltage values V _cr and λ at all voltage collapse points, the probability distribution curves obeyed are respectively fitted by kernel density estimation, and the fitting parameters of the probability distribution curves are obtained.

4. Multiple times of fitting, and then obtain the membership function of the fitting parameters of the voltage value V _cr and the lambda value probability distribution function at the voltage collapse point respectively. Repeat step 1, step 2 and step 3, take different shape parameters ^ki and scale parameters ^ci groups corresponding to the generated fan output multiple times to fit the probability distribution of the voltage value V _cr and λ value at the voltage collapse point, and then Then find out the membership function of its probability distribution fitting coefficient.

5. Use the chance measure to describe the random fuzzy characteristics of the voltage collapse point of fluctuations.

The invention mainly studies the voltage collapse point of random fuzzy and uncertain injection of wind power output into the power system. In the random fuzzy injection of wind power output into the power system, the P-V characteristic curve of the galloping system is obtained according to the continuous power flow, the voltage collapse point of each P-V curve is found, and the probability distribution of the voltage value and λ value at the collapse point is respectively fitted, The membership function of the parameters of the probability distribution function is found out by multiple fitting, and the random fuzzy characteristics of the voltage collapse point are described by the chance measure, and the uncertainty of the fluctuating voltage collapse point is described. This method can provide a new way of thinking for the static security domain of the power system in the future, provide a basis for obtaining a power system security domain that is more in line with the actual wind power uncertainty injection, and serve as an important reference for power dispatchers to carry out safe and stable operation of the power system. Under the premise of satisfying system security, the utilization rate of wind power can be reasonably improved.

Description of drawings

Fig. 1 is the general flowchart of the model of the present invention;

Figure 2 is the PV characteristic curve;

Fig. 3 is the probability distribution of the voltage value V _cr at the voltage collapse point;

Figure 4 is the probability distribution of the λ value of the voltage collapse point;

Fig. 5 is the frequency figure of V _cr fitting coefficient mean value μ _V ;

Fig. 6 is a V _cr fitting coefficient b _V frequency diagram;

Fig. 7 is λ fitting coefficient mean value μ _λ frequency figure;

Fig. 8 is a graph of the average value b _λ of the fitting coefficient of λ.

detailed description

The present invention comprises the following steps:

1. Stochastic fuzzy uncertainty modeling of wind power output

1) Establish a random fuzzy uncertainty model of wind speed

According to the actual multi-year wind speed data, the probability distribution shape parameter k of the extracted daily wind speed probability distribution parameters can be represented by a triangular fuzzy variable ξ _k = (1.14,1.75,3.64), and the scale parameter c can be expressed by a trapezoidal fuzzy variable ξ _c = (2.95 , 4.40, 6.40, 8.22), the corresponding membership functions can be expressed by formulas (1) and (2) respectively:

According to the fuzzy and uncertain characteristics of daily wind speed probability distribution parameters extracted above and their membership functions, a random fuzzy and uncertain model of daily wind speed is established, and a parameter that satisfies Pos{ } > 0 is extracted in the respective confidence intervals of parameters k and c. Group k and c values, and check whether the group k and c have k<c, if so, meet the requirements, otherwise, re-extract; then for the group k and c values, simulate and generate 1000 wind speed values v _j (where j = 1, 2, . . . , 1000.).

2) Establish the wind turbine active output model

The mechanical power of a wind turbine can be expressed as:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; u</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&cup;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; ></span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; u</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; u</span>}\mathrm{<span\; class="notranslate">\; t</span>}}\\ \mathrm{<span\; class="notranslate">\; 0.5</span>}{\mathrm{<span\; class="notranslate">\; \&rho;AC</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}& {\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; u</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; d</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; d</span>}}& {\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; u</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; u</span>}\mathrm{<span\; class="notranslate">\; t</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

In the formula: ρ is the air density, A is the rotor surface area, v is the wind speed, C _p is the power coefficient, and P is the rated active power. v _cut-in is the cut-in wind speed, v _cut-out is the cut-out wind speed, and v _rated is the rated wind speed. Substitute the wind speed value v _j corresponding to a set of parameters k and c in the random fuzzy wind speed model into the wind power active output model to calculate the active output P _j of the fan corresponding to the set k and c. From mathematical knowledge, we can know that the wind power active output is also a random Fuzzy variables, based on which the stochastic fuzzy uncertain model of wind power is obtained.

2. Establish a continuous power flow model for stochastic fuzzy wind power access to the power system

The power flow equation with parameters for the steady-state behavior of the power system is:

f(x,λ)=0(4)

In the formula, f is the general form of the power flow equation; x is the variable to be obtained composed of all node voltages and phase angles in the system; λ is the variable parameter of interest in the system (here refers to the multiplier of the load change rate at the node) .

The parametric power flow equation can be solved by the Newton-Raphson method, and its first-order Taylor expansion is:

f′ _x ·d _x +f′ _λ ·d _λ =0(5)

where f′ _x , f′ _λ respectively represent the Jacobian matrix of the power flow equation about x and the partial derivative vector about λ. When the Jacobian matrix is non-singular, equation (5) can be transformed into:

d _x ＝-f _x ^′-1 f′ _λ d _λ (6)

The power flow equation (4) with parameters can track the change of the system state by gradually increasing (or decreasing) the value of the parameter λ with the continuous power flow method, and obtain the steady solution curve of the system; when the parameters change to the limit value, the power flow equation will appear ill-conditioned (At this time f _x ′=0), that is, the saddle bifurcation point is reached mathematically, and the PV characteristic curve of the system can be obtained to find out the voltage collapse point for safety analysis.

For the random fuzzy output of wind power injected into the power system, the continuous power flow calculation is performed successively on the fan output _Pj corresponding to this set of parameters k and c as the random fuzzy continuous power flow, and a cluster of PV curves is obtained, as shown in Figure 2. Find out the voltage collapse point of each PV curve, and obtain the voltage value V _cr and λ value of a group of voltage collapse points corresponding to the group of random fuzzy parameters k and c.

3. Fit the probability distribution of the voltage value V _cr and the λ value at the voltage collapse point respectively

According to the fan output corresponding to this set of parameters k and c, a set of voltage collapse points obtained through continuous power flow calculation, the voltage value V _cr and λ value of each voltage collapse point are taken out, and the kernel density estimation is used to estimate the voltage value V The probability distribution that the _cr and λ values obey is fitted, and the randomness of the voltage value V _cr and the λ value at the voltage collapse point can be fitted with a normal distribution function, as shown in Fig. 2 and Fig. 3 . Then extract the coefficients σ _V , μ _V of the probability distribution of the voltage value V _cr and the coefficients σ _λ , μ _λ of the probability distribution of the λ value

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; V</span>}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; V</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

Simple deformation of formulas (7) and (8) respectively:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; V</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; V</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

It is not difficult to know that there are: We take the coefficients μ _V , b _V and the coefficients μ _λ , b _λ of the probability distribution that the voltage value V _cr obeys respectively for research. The fitting results are shown in Figure 3 and Figure 4.

4. Multiple fitting to obtain the membership function of the voltage value V _cr and the lambda value at the voltage collapse point of the probability distribution

According to the wind speed random fuzzy uncertainty model, select a set of parameters k and c to carry out the above steps 1, 2 and 3 to obtain a set of voltage collapse point voltage value V _cr and probability distribution coefficient of λ value; now 500 sets of parameters k ⁱ and c ⁱ (where i=1,2,…,500.) correspond to the generated fan output group The continuous power flow calculation is carried out successively, and the coefficients of the probability distribution of the voltage value V _cr and the λ value of each group of voltage collapse points are obtained. Correspondingly, 500 sets of fitting coefficients μ _V , b _V and μ _λ , b _λ are generated, and the frequency of their numerical values is counted. It is found that the fitting coefficients fluctuate within a certain range, which is ambiguous. use To describe the fuzzy characteristics of its fitting coefficients. Observing Figure 5 and Figure 6, trapezoidal fuzzy variables can be used triangular fuzzy variable Describe the fuzzy characteristics of the probability distribution fitting coefficient of the voltage value V _cr at the voltage collapse point; its membership functions are respectively:

Observing Figure 7 and Figure 8, trapezoidal fuzzy variables can be used triangular fuzzy variable Describe the fuzzy characteristics of the probability distribution fitting coefficient of the λ value of the voltage collapse point; the membership functions are:

5. Use the chance measure to describe the probability distribution of the voltage value V _cr and the λ value at the voltage collapse point of the fluctuation

According to the stochastic fuzzy model of the voltage collapse point obtained above, the random fuzzy characteristics of the voltage collapse point are described by the chance measure distribution function:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}<span\; class="notranslate">\; ,</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}}_{\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; ,</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; b</span>}}}_{\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}^{{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}}{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}}_{\mathrm{<span\; class="notranslate">\; V</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}}_{\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; b</span>}}}_{\mathrm{<span\; class="notranslate">\; V</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 15</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; ,</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; ,</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; b</span>}}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}^{{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}}{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}}_{\mathrm{<span\; class="notranslate">\; V</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; b</span>}}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; )</span>$

in Indicates that the voltage collapse point is a random fuzzy variable, that is, a fluctuating voltage collapse point is obtained.

Based on this, a method for calculating the voltage collapse point of power system fluctuations with random fuzzy injection of wind power is obtained.

The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Those of ordinary skill in the relevant technical field can also make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all Equivalent technical solutions also belong to the protection category of the present invention.

Claims (6)

1. inject a collapse of voltage point acquiring method for electric system fluctuation containing wind-powered electricity generation Random-fuzzy, it is characterized in that:

(1) wind power output stochastic uncertainty modeling;

(2) the Random-fuzzy continuous tide of wind power integration electric system calculates;

(3) the magnitude of voltage V at matching collapse of voltage point place _crthe probability distribution of obeying with λ value;

(4) repeatedly the magnitude of voltage V at probability distribution collapse of voltage point place is asked in matching _crwith the membership function of λ value;

(5) the Random-fuzzy characteristic of the collapse of voltage point of fluctuation is described with chance measure.

2. a kind of collapse of voltage point acquiring method injecting electric system fluctuation containing wind-powered electricity generation Random-fuzzy according to claim 1, it is characterized in that, described step 1) middle wind power output undefinitude of random-fuzzy modeling, first to set up wind speed Random-fuzzy ambiguous model, choose one group of meet the requirements form parameter k and scale parameter c group to obtain Random-fuzzy wind speed; Again according to wind power output model, obtain wind-powered electricity generation Random-fuzzy and exert oneself.

3. a kind of collapse of voltage point acquiring method injecting electric system fluctuation containing wind-powered electricity generation Random-fuzzy according to claim 1, it is characterized in that, described step 2) the middle Random-fuzzy continuous tide calculating considering wind power integration electric system, one-time continuous Load flow calculation is carried out with a wind power output size correspondence, obtain a PV curve, and find the magnitude of voltage V at collapse of voltage point place _crwith λ value; To exert oneself size according to one group of wind-powered electricity generation Random-fuzzy of trying to achieve, obtain the magnitude of voltage V at the PV curve under all wind power output levels, collapse of voltage point place _crwith λ value, calculate as Random-fuzzy continuous tide.

4. a kind of collapse of voltage point acquiring method injecting electric system fluctuation containing wind-powered electricity generation Random-fuzzy according to claim 1, is characterized in that, described step 3) according to the magnitude of voltage V at all collapse of voltage point places obtained _crwith λ value, simulate its probability distribution curve of obeying respectively by Density Estimator, obtain the fitting parameter of probability distribution curve.

5. a kind of collapse of voltage point acquiring method injecting electric system fluctuation containing wind-powered electricity generation Random-fuzzy according to claim 1, it is characterized in that, described step 4) in repeat in claim 1 step 1), step 2) and step 3), to exert oneself repeatedly the magnitude of voltage V at matching collapse of voltage point place getting difformity parameter and the blower fan of scale parameter group to generation _crthe probability distribution of obeying with λ value, and then the membership function finding out its Probability Distribution Fitting coefficient.

6. a kind of collapse of voltage point acquiring method injecting electric system fluctuation containing wind-powered electricity generation Random-fuzzy according to claim 1, is characterized in that, described step 5) the Random-fuzzy characteristic of the collapse of voltage point of fluctuation is described with chance measure function.